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A Generalized Approach for Reducing Expensive Distance Calls for A Broad Class of Proximity Problems

Summary: Generalized framework to reduce expensive distance calls in proximity problems by modeling distance comparisons as linear inequalities. Graph-based solutions plus a practitioner guide; experiments on large real-world datasets demonstrate reduced distance calls. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6255
Venue
SIGMOD
Year
2021
Pagerank
5.3577837e-05
Overall Rank
8,848 | 39.30%
DOI
10.1145/3448016.3457303

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{augustine_sigmod21,
        title = {{A Generalized Approach for Reducing Expensive Distance Calls for A Broad Class of Proximity Problems}},
        author = {Augustine, Jees and Shetiya, Suraj and Esfandiari, Mohammadreza and Roy, Senjuti Basu and Das, Gautam},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457303},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457303},
        year = {2021}
}

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Rank Citing Paper Year Venue Pagerank
9,353 On Efficient Approximate Queries over Machine Learning Models 2023 VLDB 5.2829539e-05
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